English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The computational problem of inferring the full haplotype of a cell startingfrom read sequencing data is known as haplotype assembly, and consists inassigning all heterozygous Single Nucleotide Polymorphisms (SNPs) to exactlyone of the two chromosomes. Indeed, the knowledge of complete haplotypes isgenerally more informative than analyzing single SNPs and plays a fundamentalrole in many medical applications. To reconstruct the two haplotypes, weaddressed the weighted Minimum Error Correction (wMEC) problem, which is asuccessful approach for haplotype assembly. This NP-hard problem consists incomputing the two haplotypes that partition the sequencing reads into twodisjoint sub-sets, with the least number of corrections to the SNP values. Tothis aim, we propose here GenHap, a novel computational method for haplotypeassembly based on Genetic Algorithms, yielding optimal solutions by means of aglobal search process. In order to evaluate the effectiveness of our approach,we run GenHap on two synthetic (yet realistic) datasets, based on the Roche/454and PacBio RS II sequencing technologies. We compared the performance of GenHapagainst HapCol, an efficient state-of-the-art algorithm for haplotype phasing.Our results show that GenHap always obtains high accuracy solutions (in termsof haplotype error rate), and is up to 4x faster than HapCol in the case ofRoche/454 instances and up to 20x faster when compared on the PacBio RS IIdataset. Finally, we assessed the performance of GenHap on two different realdatasets. Future-generation sequencing technologies, producing longer readswith higher coverage, can highly benefit from GenHap, thanks to its capabilityof efficiently solving large instances of the haplotype assembly problem.

GenHap: A Novel Computational Method Based on Genetic Algorithms for Haplotype Assembly